Rotary tillage depth detection device, system and method

Abstract

The invention discloses a rotary tillage tilling depth detection device, system and method in the field of tilling depth detection, and the device comprises an acquisition module, a data processing module for processing data, a cloud server for storing the data, a terminal upper computer for receiving the data, and a main controller. The data processing module comprises a data processing algorithm used for calculating the depth of a plowing layer according to collected plowing pressure data, the main controller is used for outputting an instruction to the servo motor and transmitting the data to a display screen of the terminal upper computer, and the display screen is used for inputting parameters processed by the data processing module. The display screen is also provided with a visual window, and the visual window is used for displaying a data processing result. According to the data processing algorithm, the average lag value is obtained by processing a large amount of data under the simulation condition, the lag phenomenon of hardware can be eliminated, the preset value is used for processing the data under the field condition, and interference data under the field condition is eliminated.

Description

Technical Field

[0001] The present invention relates to the field of tillage depth detection, and particularly to a rotary tillage depth detection device, system and method. Background Art

[0002] With the gradual improvement of the level of agricultural mechanization in China in recent years, the identification and detection of the operating performance of agricultural machinery before leaving the factory have become a hot research topic. For the land rotary-tilled by a rotary tiller equipped with a tractor, the tillage layer depth can reflect whether the identified rotary tillage machinery meets the production requirements. The traditional tillage depth detection method mainly relies on appraisers going into the field for measurement, which is highly subjective and the recorded data is difficult to save. The current relevant research mainly focuses on improving the working performance of tillage components. The devices for detecting the operating performance of a rotary tiller equipped with a tractor are all installed on the rotary tiller for data acquisition, which cannot ensure the stability of the detection work, the integrity of the collected data points, and the safety of appraisers.

[0003] After processing the collected data to obtain result data (tillage layer depth), since there are still some interference data in the collected data and the hardware used in the data processing process will have a lag phenomenon, both will affect the result data obtained in the subsequent processing, and there will be a problem of large error in the result data. Summary of the Invention

[0004] The purpose of the present invention is to provide a rotary tillage depth detection device, system and method. First, an average lag value is obtained through data processing under a large number of simulation conditions. When dealing with field conditions, the average lag value is used to eliminate the lag phenomenon of the hardware.

[0005] To solve the above technical problems, the following technical solutions are adopted:

[0006] In the first aspect, the present invention provides a rotary tillage depth detection device, including a servo motor, a probe, a pressure sensor and a bracket. The probe is installed on the bracket, and the servo motor is used to drive the probe to move on the bracket. The probe is parallel to the bracket. One end of the probe is provided with the pressure sensor, and the other end is a conical probe. A foot pedal is provided at the bottom of the bracket.

[0007] In the second aspect, the present invention provides a rotary tillage depth detection system, including a collection module. The collection module includes the rotary tillage depth detection device described in the first aspect, and also includes a data processing module for processing data, a cloud server for storing data, a terminal host computer for receiving data and a main controller;

[0008] The data processing module includes a data processing algorithm for calculating the tillage layer depth based on the collected arable land pressure data. The main controller is used to output instructions to the servo motor and transmit the data to the display screen of the terminal upper computer. The display screen is used to input the parameters processed by the data processing module, and a visualization window is also provided on the display screen. The visualization window is used to display the data processing results.

[0009] In a third aspect, the present invention provides a rotary tillage depth detection method, which is implemented by using the rotary tillage depth detection system described in the second aspect, and includes the following steps: Collect the first raw data under the simulation conditions with a known tillage layer depth, perform the filtering processing step in the data processing algorithm on the first raw data to obtain the optimal filtering parameters; the optimal filtering parameters are used as the input parameters of the data processing algorithm, and the data processing algorithm is run in combination with the first raw data to obtain the average lag value; Collect the second raw data under the field conditions. The average lag value and the optimal filtering parameters are used as the input parameters of the data processing algorithm, and the data processing algorithm is run in combination with the second raw data to generate the target data; When the data processing algorithm processes the second raw data, it also includes a step of excluding interference data under field conditions based on preset value parameters.

[0010] Optionally, the specific steps for obtaining the optimal filtering parameters include: After filtering the first raw data with a Kalman filter, compare it with the first raw data; Modify the filtering parameters, and use the modified filtering parameters to filter the first raw data again until the filtered first raw data is closest to the first raw data to obtain the optimal filtering parameters.

[0011] Optionally, the specific steps for obtaining the average lag value include: Filter the first raw data with a Kalman filter input with the optimal filtering parameters; Perform high-order curve fitting on the filtered first raw data to obtain the fitted curve; Compare the first raw data with the curve and record the lag value; Statistically analyze multiple groups of the lag values and calculate the average lag value.

[0012] Optionally, after obtaining the average lag value, perform average lag value parameter verification and preset value parameter verification, which specifically include the following steps: The first data executes a data processing algorithm with an input average lag value and outputs a first tillage layer depth, which is compared with a known tillage layer depth, and the error is within the allowable range; The first data executes a data processing algorithm with an input average lag value and a preset value, outputs the first tillage depth again, and the first tillage layer depth output again is compared with a known tillage layer depth, and the error is within the allowable range.

[0013] Optionally, the specific steps for generating the target data are as follows: Filter the second raw data using a Kalman filter with optimal filtering parameters input; Perform high-order curve fitting on the filtered second raw data to obtain a first curve; Subtract the average lag value from the first curve to obtain a second curve; Use the soil stratification based on derivative features on the second curve to find feature points, and calculate the tillage layer depth corresponding to the feature points. The tillage layer depth is the target data.

[0014] Optionally, calculating the tillage layer depth corresponding to the feature points includes the following steps: Calculate the distance difference between the position of the feature point and the position where the probe contacts the soil. After subtracting the height of the conical probe, the measured tillage layer thickness can be obtained; ; In the formula: H is the tillage layer thickness; x t is the probe depression distance corresponding to the feature point; x o is the probe depression distance corresponding to the position where the conical probe contacts the soil; h e is the height of the conical probe body of the acquisition device.

[0015] Optionally, using the soil stratification based on derivative features on the second curve to find feature points specifically includes the following steps: Calculate the first derivative and the second derivative of the curve, represented by p and q; ; ; Among them, the x coordinate of the curve represents the probe depression distance of the acquisition device, and the y axis coordinate represents the pressure value; Then traverse the maximum points of the first derivative and find the largest maximum point p m ; Find the p m The first zero crossing of the downward trend of the first derivative after the point, that is, at this point p = 0, q < 0, and this point is the feature point; Among them, finding the starting position where the pressure data in the curve is stably greater than 0 is the position where the probe of the detection device contacts the soil.

[0016] Optionally, the step of excluding interference data under field conditions based on preset parameter values includes: Setting a threshold for the tillage layer depth when finding feature points. When the tillage layer depth calculated for the found feature point is less than the threshold, ignoring the feature point corresponding to the calculated tillage layer depth and continuing to find the subsequent feature points.

[0017] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0018] 1. The rotary tillage depth detection device provided by the present invention detects the soil by contacting it with the conical probe at the end of the probe. The force between the conical probe and the soil is detected by the pressure sensor at the other end. The pedal provided on the bracket can stabilize the device and serve as a reference plane during detection, improving the detection accuracy.

[0019] 2. The rotary tillage depth detection system provided by the present invention calculates and outputs the tillage layer depth data from the collected pressure data, improving the tillage depth detection efficiency. It includes a terminal host computer, which can observe the data being processed and output the parameters obtained under simulation conditions, optimizing the data processing algorithm.

[0020] 3. The rotary tillage depth detection method submitted by the present invention obtains the average hysteresis value by processing the data collected under simulation conditions, and uses the average hysteresis value to eliminate the error caused by the filter. When processing the data under field conditions, a preset value processing is also set to exclude some interference factors under field conditions, improving the data accuracy. Description of the Drawings

[0021] Figure 1 is a schematic structural diagram of the rotary tillage depth detection device in an embodiment of the present invention; Figure 2 is a schematic framework diagram of the rotary tillage depth detection system in an embodiment of the present invention; Figure 3 is a schematic visualization window diagram of the rotary tillage depth detection system in an embodiment of the present invention; Figure 4 is a chart of the first raw data obtained under simulation conditions in an embodiment of the present invention; Figure 5 is a chart of the second raw data obtained under field conditions in an embodiment of the present invention; Figure 6 is a data chart when feature points are selected in an embodiment of the present invention; Figure 7 is a visualization effect diagram of the detection result in an embodiment of the present invention.

[0022] Description of the drawing reference numerals: 1. Servo motor; 2. Pressure sensor; 3. Probe; 4. Bracket; 5. Pedal. Specific embodiments

[0023] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and in no way limits the present invention and its application or use. Embodiment

[0024] As Figure 1 shown, this embodiment provides a rotary tillage depth detection device, including a servo motor 1, a probe 3, a pressure sensor 2 and a bracket 4. The servo motor 1 is installed on the top of the bracket 4. One end of the probe 3 provided with the pressure sensor 2 is installed on the bracket 4. The probe 3 is arranged parallel to the bracket 4. A foot pedal 5 is provided at the bottom of the bracket 4. The probe 3 is driven by the servo motor 1 to move on the bracket 4. The bottom of the probe is a conical probe for piercing into the soil.

[0025] During use, the device is vertically placed on the soil to be detected. The pedal 5 plays a stabilizing role and provides a reference surface for data collection. During the detection process, the servo motor 1 pushes the probe 3 and the pressure sensor 2 to perform a downward pressing movement, collecting multi-point data of the same piece of tillage land. The servo motor is used to push the probe and the pressure sensor to press down evenly with a constant force of 100 N·M. The starting position is a certain distance above the ground, and the ending position ensures that the probe is completely immersed in the uncultivated layer, collecting relevant data of the cultivated layer and the uncultivated layer.

[0026] During the downward pressing process of the probe, the pressure value measured by the pressure sensor is actually the vertical upward force generated by the reaction force of the soil on the probe when the probe contacts the soil. This force is affected by multiple parameters such as the cohesion of the soil, the effective normal stress, and the contact area between the conical probe and the soil. Here, use to represent the measured pressure value.

[0027] When the conical probe enters the soil, its contact area with the soil increases as the penetration depth increases. The calculation formula is as follows: ; ; In the formula: is the contact area between the conical probe and the soil, with the unit of m 2 ; is the contact radius between the conical probe and the soil, with the unit of m; h is the penetration depth of the conical probe into the soil, with the unit of m; is the semi-vertex angle of the cone penetrometer, which is a fixed value with the unit of degree.

[0028] The shear strength τ of the soil is an important factor affecting the frictional force between the soil and the penetrometer. Its calculation formula is as follows: ; ; In the formula: c is the soil cohesion with the unit of N / m 2 , which is a constant depending on the soil type; is the effective normal stress with the unit of N / m 2 ; φ is the internal friction angle of the soil, which is a constant related to the soil type, water content, etc., with the unit of degree; is the pressure of the cone penetrometer on the soil with the unit of N.

[0029] After the cone penetrometer enters the soil, it will be subject to frictional force , with the unit of N, and its calculation formula is as follows: ; And the pressure value measured by the pressure sensor includes the vertical components of the normal support force and the frictional force. Its calculation formula is as follows: ; ; In the formula: is the force measured by the pressure sensor with the unit of N; is the normal support force of the soil on the cone penetrometer, which is the same in magnitude and opposite in direction to , with the unit of N; is the semi-vertex angle of the cone penetrometer with the unit of degree.

[0030] The data of the sensor pressure value and the travel distance of the cone penetrometer are as Figure 5 shown. From the above formulas, it can be seen that theoretically, when the cone penetrometer is pressed down to different positions in the soil, the change of the pressure value measured by the system conforms to the following rules: 1. When the detection device starts to move, the pressure value remains 0. When the penetrometer is pressed down to contact the tillage layer soil, as the cone penetrometer sinks into the tillage layer, the contact area between the penetrometer and the soil will gradually increase, resulting in the first increase in the measured pressure value.

[0031] 2. When the penetrometer completely enters the tillage layer, the contact area reaches the maximum, and all parameters are constant, resulting in the phenomenon that the measured pressure value tends to be stable.

[0032] 3. When the probe touches the non-plowed layer, the soil cohesion c increases, resulting in an increase in the measured pressure value again. At the same time, the increase in the contact area causes the same pressure change as in Rule 1. The superposition of these two factors makes the amplitude of the second increase in the measured pressure value larger than the first time.

[0033] 4. When the probe is completely immersed in the non-plowed layer, due to the constant parameters, the measured pressure value will tend to be stable again, but at this time the pressure value is still significantly higher than the pressure value when the probe just touches the non-plowed layer.

[0034] Based on this, the collected data is processed and analyzed using the soil layer feature point recognition method based on derivative features. The demarcation point between the plowed layer and the non-plowed layer is found in the pressure data, and the depth of the plowed layer is calculated in combination with the travel distance information transmitted back by the servo motor.

[0035] Embodiment 2

[0036] As Figure 2 shown, this embodiment provides a rotary tillage depth detection system, including a collection module, a data processing module, a cloud server, a terminal host computer, and a main controller. The collection module includes the rotary tillage depth detection device described in Embodiment 1. The ESP32-S3 Internet of Things development board is used as the main controller, and the rotary tillage depth detection device communicates with the terminal host computer through a network communication protocol. The terminal host computer is a computer equipped with Windows 11, a 64-bit operating system.

[0037] The main controller outputs a control instruction to the servo motor, and the servo motor executes the instruction. The travel distance of the conical probe and the data of the pressure sensor are transmitted to the main controller. The main controller statistically processes the received data and displays it on the display screen of the terminal host computer. At the same time, the data of the main controller is transmitted to the cloud server. The cloud server stores the data and transmits it to the data processing module for processing by the data processing module.

[0038] As Figure 3 shown, the processing results during the processing are displayed on the visualization window of the display screen. The display screen of the terminal host computer can also input the parameters in the data processing module to correct the data processing algorithm in the data processing module, making the processed data more accurate.

[0039] The data processing algorithm filters, fits, and finds feature points for the received data, and outputs the depth of the plowed layer.

[0040] Embodiment 3

[0041] This embodiment provides a rotary tillage depth detection method, which is implemented using the rotary tillage depth detection system provided in Embodiment 2, and includes the following steps: Collect the first raw data under simulation conditions with a known tillage layer depth, perform the filtering processing step in the data processing algorithm on the first raw data to obtain the optimal filtering parameters; use the optimal filtering parameters as the input parameters of the data processing algorithm, and run the data processing algorithm in combination with the first raw data to obtain the average lag value; Collect the second raw data under field conditions. Use the average lag value and the optimal filtering parameters as the input parameters of the data processing algorithm, and run the data processing algorithm in combination with the second raw data to generate the target data; When the data processing algorithm processes the second raw data, it also includes a step of excluding interference data under field conditions based on preset value parameters.

[0042] The data processing algorithm includes filtering the data of the pressure value of the cone penetrometer and the travel distance of the cone penetrometer, fitting them into a curve and finding the characteristic points, and finally outputting the tillage layer depth.

[0043] Under simulation conditions, the tillage layer is artificially laid in the laboratory. Use the acquisition device to collect data on the laid tillage layer. When the tillage layer depth is known, use the detection system in Example 2 to process the collected first raw data to obtain a tillage layer depth. Compare the calculated tillage layer depth with the known artificially laid tillage layer depth, and count and analyze the error.

[0044] In the filtering processing step of the data processing algorithm for the first raw data, continuously modify the filtering parameters to make the first raw data after filtering processing closest to that before processing, and obtain the optimal filtering parameters. Use the optimal filtering parameters to process a large amount of first raw data, compare the output tillage layer depth with the known tillage layer depth, and count and calculate the average lag value in the process of the data processing algorithm.

[0045] For the second raw data under field conditions, during the processing by the data processing algorithm, use the optimal filtering parameters and the average lag value obtained under the above simulation conditions as parameter inputs to process the second raw data in the data processing algorithm. The calculated tillage layer depth error under field conditions is small, thus improving the detection accuracy.

[0046] Moreover, for some factors under field conditions, such as the soil clods not being broken, the pressure value of the cone penetrometer will mutate when it pierces into the soil clods, or there are stones in the tillage layer, or the surface of the tillage layer is dried, resulting in a mutation of the pressure value, etc. The tillage layer depth calculated by the mutation of the pressure value through the data processing algorithm needs to be ignored. Therefore, the data processing algorithm also includes a step of setting a preset value to process the second raw data. Through the set preset value, the calculated tillage layer depth needs to be within the preset value range to be recorded, so as to improve the detection accuracy.

[0047] Example 4

[0048] Based on Embodiment 3, this embodiment provides a rotary tillage depth detection method, with the difference being that: During the filtering step in the processing of the first raw data by the data processing algorithm, specifically: A Kalman filter is used to filter the first raw data, and the filter formula is as follows: ; Covariance prediction formula: ; Kalman gain: ; State update formula: ; Covariance update formula: ; In the formula: is the predicted state at the current moment; is the state estimate at the previous moment; is the predicted covariance at the current moment; is the predicted covariance at the previous moment; Q is the process noise covariance; K is the Kalman gain; R is the measurement noise covariance; is the state estimate at the current moment; is the measurement value at the current moment; is the residual between the predicted value and the measurement value; is the covariance estimate at the current moment.

[0049] The first raw data before and after processing is compared, and then the Q, K, and R parameters in the filtering process are modified. After one modification, the first raw data is filtered again, and then compared. After multiple modifications and comparisons, until the data after processing the first raw data is closest to the data before processing, the filtering parameters used this time are obtained as the optimal filtering parameters.

[0050] Filter the first raw data using the optimal filtering parameters and fit the processed first raw data into a curve.

[0051] Compare the obtained curve with the points of the same trend on the corresponding first original data. Since the filter has a lag phenomenon, there is a lag value when comparing the processed first original data with the unprocessed one. Record the lag value. Since the lag phenomenon caused by the filter is relatively stable, perform the above processing on multiple groups of first original data to obtain multiple groups of curves. Compare each group of curves with the corresponding first original data, record the lag values, and statistically analyze the lag values of the processed multiple groups of first original data to calculate the average lag value. The average lag value is the value at the same depth of different measurement points.

[0052] After obtaining the average lag value, use the first original data for verification. Specifically: use the average lag value as a processing parameter in the data processing algorithm to process the first original data. Filter the first original data using the optimal filtering parameters, fit the processed data to obtain a smooth curve. Subtract the average lag value corresponding to the same trend on the curve from the data on the curve to obtain the processed curve. Then find the characteristic points on the curve and calculate the tillage layer depth corresponding to the characteristic points. Since the first original data is collected under simulation conditions, the depth under simulation conditions is artificially laid in advance and is known. Compare the calculated tillage layer depth with the known tillage layer depth.

[0053] Table 1

[0054]

[0055] As shown in Table 1, under simulation conditions, tillage layers of 6 cm, 8 cm, and 10 cm are artificially laid to verify the data processing algorithm after inputting the average lag value. For each depth of the tillage layer, 10 groups of data are collected. The collected data is input into the data processing algorithm for calculation, and the corresponding 1 - 10 tillage layer depths are output and compared with the tillage layer of that depth. Record the error, statistically analyze the average error value of each depth of the tillage layer, calculate the overall average error value, and the average error value is 0.413, with a relatively small error.

[0056] Process the second original data collected under field conditions using the above data processing algorithm that has input the average lag value. During the process of processing the second original data, it is found that there is a large error between the tillage layer depth output by the data processing algorithm and the artificially measured tillage layer depth. As shown in Table 2 below, the reason for the large error is that there are some uncertain factors under field conditions, such as soil clods not being broken, the pressure value mutating when the probe hits a soil clod, or there being stones in the tillage layer, or the surface of the tillage layer being dried, resulting in a mutation of the collected pressure value, so a large error occurs.

[0057] Table 2

[0058]

[0059] For the uncertainties under the above field conditions, the data processing algorithm introduces preset values to process the second raw data to exclude the above factors.

[0060] Specifically, when calculating the tillage layer depth by finding the curve feature points, set the minimum threshold of the tillage layer depth corresponding to the feature points. When the tillage layer depth calculated by this feature point is less than this minimum threshold, ignore this feature point and continue to find the next feature point.

[0061] For the data processing algorithm with preset values introduced, before processing the second raw data, verify it again using the first raw data. Specifically: The tillage layer depth output by the data processing algorithm with preset values introduced in the first raw data is compared with the known tillage layer depth. If the error is within the allowable range, verify that this preset value is valid.

[0062] The verified data processing algorithm is verified again under field conditions. Under field conditions, the tillage layer depth data is obtained through manual measurement, and the tillage layer depth data is calculated using the above data processing algorithm. The two data are compared and the error is not large, as shown in Table 3 below.

[0063] Table 3

[0064]

[0065] The above data processing algorithm determines that the parameters of the data processing algorithm are input on the terminal upper computer module, including the optimal filtering parameter, average lag value, and minimum threshold. Then, the second raw data is collected under field conditions, and the collected data is calculated by the data processing algorithm with the determined parameters to obtain the tillage layer depth. Specifically: Use the Kalman filter with the optimal filtering parameter input to filter the second raw data; As Figure 6 shown, perform high-order curve fitting on the filtered second raw data to obtain the first curve; Subtract the average lag value from the first curve to obtain the second curve; Calculate the first derivative and second derivative of the second curve, represented by p and q; ; ; Among them, the x coordinate of the second curve represents the cone pressing distance of the acquisition device, and the y-axis coordinate represents the sensor pressure value; Then traverse the maximum points of the first derivative and find the largest maximum point p m ; Find p mThe zero-crossing point of the first-order derivative decline trend after the point, that is, at this point p = 0 and q < 0, and this point is the characteristic point; Among them, finding the starting position where the pressure data in the curve is stably greater than 0 is the position where the probe of the detection device contacts the soil, as Figure 7 shown.

[0066] Calculate the distance difference between the position of the characteristic point and the position where the probe contacts the soil. After subtracting the height of the cone probe, the measured tillage layer thickness can be obtained; ; In the formula: H is the tillage layer thickness; x t is the probe pressing distance corresponding to the characteristic point; x o is the probe pressing distance corresponding to the position where the cone probe contacts the soil; h e is the cone height of the probe of the acquisition device.

[0067] The rotary tillage depth detection method provided in this embodiment processes the data under simulation conditions through a data processing algorithm to obtain the error of the filter parameters, that is, the average lag value. After obtaining the average lag value, the data under simulation conditions is used for verification. The preset value in the data processing algorithm is verified using the data under simulation conditions. After verifying that the use of the preset value can improve the data accuracy, it is then verified under field conditions. The result calculated by the data processing algorithm is compared with the result of manual measurement, and the error is smaller than that of the data processing algorithm without setting the preset value.

[0068] In the description of the present invention, it should be understood that the orientation or positional relationships indicated by the terms "center", "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. are based on the orientation or positional relationships shown in the drawings. These are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation on the present invention. In addition, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first", "second", etc. may explicitly or implicitly include one or more of such features. In the description of the present invention, unless otherwise specified, the meaning of "a plurality" is two or more. In the description of the present invention, it should be noted that unless otherwise clearly specified and limited, the terms "mounted", "connected", "coupled" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood through specific circumstances.

[0069] The above are only the preferred embodiments of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and modifications can still be made, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A rotary tillage depth detection device, characterized in that: It includes a servo motor, a probe, a pressure sensor and a bracket. The probe is installed on the bracket and driven by the servo motor to move on the bracket. The probe is parallel to the bracket. The pressure sensor is arranged on one end of the probe and the other end is a conical probe. A foot pedal is arranged at the bottom of the bracket.

2. A rotary tillage depth detection system, comprising a collection module, wherein the collection module comprises the rotary tillage depth detection device according to claim 1, characterized in that: It also includes a data processing module for processing data, a cloud server for storing data, a terminal host computer and a main controller for receiving data; The data processing module includes a data processing algorithm for calculating the depth of the tillage layer based on the collected arable land pressure data. The main controller is used to output instructions to the servo motor and transmit data to the display screen of the terminal host computer. The display screen is used to input parameters processed by the data processing module. A visualization window is also provided on the display screen, and the visualization window is used to display the data processing results.

3. A method for detecting the tillage depth of rotary tillage, implemented by the rotary tillage depth detection system according to claim 2, characterized in that: The following steps are involved: Collecting first raw data under simulation conditions where the tillage layer depth is known, performing a filtering process in a data processing algorithm on the first raw data to obtain an optimal filtering parameter; using the optimal filtering parameter as an input parameter of the data processing algorithm, and running the data processing algorithm in combination with the first raw data to obtain an average lag value; Collecting second original data under field conditions, using the average lag value and the optimal filtering parameter as input parameters of the data processing algorithm, and running the data processing algorithm in combination with the second original data to generate target data; When the data processing algorithm processes the second original data, it also includes a step of eliminating interference data under field conditions based on preset value parameters.

4. The rotary tillage depth detection method according to claim 3, characterized in that: The specific steps of obtaining the optimal filtering parameters include: After filtering the first original data using a Kalman filter, the first original data is compared with the first original data; Modify the filtering parameters, and use the modified filtering parameters to filter the first original data again until the first original data after filtering is closest to the first original data, so as to obtain the optimal filtering parameters.

5. The rotary tillage depth detection method according to claim 3, characterized in that: The specific steps of obtaining the average hysteresis value include: Using a Kalman filter with optimal filtering parameters input to perform filtering processing on the first raw data; Performing high-order curve fitting on the first original data after filtering to obtain a fitted curve; The first raw data is compared with the curve, and a hysteresis value is recorded; The multiple groups of hysteresis values ​​are counted to calculate the average hysteresis value.

6. The rotary tillage depth detection method according to claim 3, characterized in that: After obtaining the average hysteresis value, the average hysteresis value parameter verification and the preset value parameter verification are performed, which specifically includes the following steps: The first data executes a data processing algorithm with an average hysteresis value input, outputs a first tillage layer depth, the first tillage layer depth is compared with a known tillage layer depth, and the error is within an allowable range; The first data executes a data processing algorithm that inputs an average lag value and a preset value, and outputs a first tillage depth again. The first tillage layer depth output again is compared with a known tillage layer depth, and the error is within an allowable range.

7. The rotary tillage depth detection method according to claim 3, characterized in that: The specific steps of generating target data are: Using a Kalman filter with optimal filtering parameters input to perform filtering processing on the second original data; Performing high-order curve fitting on the second original data after filtering to obtain a first curve; Subtracting the average hysteresis value from the first curve to obtain a second curve; On the second curve, soil stratification based on derivative characteristics is used to find characteristic points, and the tillage layer depth corresponding to the characteristic points is calculated, and the tillage layer depth is the target data.

8. The rotary tillage depth detection method according to claim 7, characterized in that: The method of calculating the tillage layer depth corresponding to the feature point comprises the following steps: Calculate the distance difference between the feature point position and the position where the probe contacts the soil, and subtract the height of the cone probe to obtain the measured thickness of the cultivated layer. ； Where: H is the thickness of the tillage layer; x t is the probe pressing distance corresponding to the characteristic point; x o h is the probe pressing distance corresponding to the position where the cone probe contacts the soil; e is the cone height of the probe of the acquisition device.

9. The rotary tillage depth detection method according to claim 7, characterized in that: The method of finding characteristic points by using soil stratification based on derivative characteristics on the second curve specifically includes the following steps: Calculating the first and second derivatives of the curve, denoted by p and q; ； ； The x-axis coordinate of the curve represents the distance the probe of the acquisition device is pressed down, and the y-axis coordinate represents the pressure value; Then traverse the maximum points of the first-order derivative and find the largest maximum point p m ; Find p m The first zero-crossing point of the downward trend of the first-order derivative after the point, that is, p=0, q<0 at this point, this point is the characteristic point; The starting position where the pressure data in the curve is stably greater than 0 is found, which is the position where the probe of the detection device touches the soil.

10. The rotary tillage depth detection method according to claim 9, characterized in that: The step of eliminating interference data under field conditions based on preset value parameters includes: When searching for feature points, a threshold of the tillage layer depth is set. When the tillage layer depth calculated by the searched feature point is less than the threshold, the feature point corresponding to the calculated tillage layer depth is ignored, and the search for the next feature point continues.

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